Three-dimensional modeling of light scattering in biological tissue by the spectral method
Bibliographic record
Abstract
For the first time the three dimensional modeling of laser light scattering in biological tissue has been performed using the spectral technique. The accuracy of the spectral numerical method has been verified by comparison with linear perturbation theory and Mie theory. Comparison with Mie theory has validated that the three-dimensional scalar wave equation is a good approximation to the full Maxwell's set of equations for light scattering at moderate angles. The computational requirements for the spectral method in modeling laser interaction with biological samples are much lower than the requirements for other existing numerical methods: finite-difference time-domain and Monte Carlo. Yet the new logarithm is capable of resolving the variations in the scattered signal with a contrast in intensity of up to six orders of magnitude. The spectral technique can be successfully applied to address to address scattering from individual cells and from biological samples containing many cells. The new method is well suited to recognize the size and composition of biological cells, making it a valuable tool in cell cytometry, for example, in the detection of rare event cells and cancerous cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".